Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations7385
Missing cells295
Missing cells (%)0.3%
Duplicate rows1033
Duplicate rows (%)14.0%
Total size in memory2.3 MiB
Average record size in memory322.6 B

Variable types

Categorical3
Text1
Numeric8

Alerts

Dataset has 1033 (14.0%) duplicate rowsDuplicates
co2_emissions_g_km_ is highly overall correlated with cylinders and 5 other fieldsHigh correlation
cylinders is highly overall correlated with co2_emissions_g_km_ and 6 other fieldsHigh correlation
engine_size_l_ is highly overall correlated with co2_emissions_g_km_ and 5 other fieldsHigh correlation
fuel_consumption_city_l_100_km_ is highly overall correlated with co2_emissions_g_km_ and 5 other fieldsHigh correlation
fuel_consumption_comb_l_100_km_ is highly overall correlated with co2_emissions_g_km_ and 5 other fieldsHigh correlation
fuel_consumption_comb_mpg_ is highly overall correlated with co2_emissions_g_km_ and 5 other fieldsHigh correlation
fuel_consumption_hwy_l_100_km_ is highly overall correlated with co2_emissions_g_km_ and 5 other fieldsHigh correlation
make is highly overall correlated with cylindersHigh correlation
transmission has 295 (4.0%) missing values Missing

Reproduction

Analysis started2025-08-18 09:17:32.020899
Analysis finished2025-08-18 09:17:37.312745
Duration5.29 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

make
Categorical

High correlation 

Distinct42
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size455.5 KiB
FORD
628 
CHEVROLET
588 
BMW
527 
MERCEDES-BENZ
 
419
PORSCHE
 
376
Other values (37)
4847 

Length

Max length13
Median length11
Mean length6.1439404
Min length3

Characters and Unicode

Total characters45373
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowACURA
2nd rowACURA
3rd rowACURA
4th rowACURA
5th rowACURA

Common Values

ValueCountFrequency (%)
FORD 628
 
8.5%
CHEVROLET 588
 
8.0%
BMW 527
 
7.1%
MERCEDES-BENZ 419
 
5.7%
PORSCHE 376
 
5.1%
TOYOTA 330
 
4.5%
GMC 328
 
4.4%
AUDI 286
 
3.9%
NISSAN 259
 
3.5%
JEEP 251
 
3.4%
Other values (32) 3393
45.9%

Length

2025-08-18T14:47:37.373306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ford 628
 
8.3%
chevrolet 588
 
7.8%
bmw 527
 
7.0%
mercedes-benz 419
 
5.6%
porsche 376
 
5.0%
toyota 330
 
4.4%
gmc 328
 
4.3%
audi 286
 
3.8%
nissan 259
 
3.4%
jeep 251
 
3.3%
Other values (35) 3555
47.1%

Most occurring characters

ValueCountFrequency (%)
E 4807
 
10.6%
O 3608
 
8.0%
A 3589
 
7.9%
R 3114
 
6.9%
I 2781
 
6.1%
D 2672
 
5.9%
N 2483
 
5.5%
C 2458
 
5.4%
S 2345
 
5.2%
M 2036
 
4.5%
Other values (17) 15480
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45373
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 4807
 
10.6%
O 3608
 
8.0%
A 3589
 
7.9%
R 3114
 
6.9%
I 2781
 
6.1%
D 2672
 
5.9%
N 2483
 
5.5%
C 2458
 
5.4%
S 2345
 
5.2%
M 2036
 
4.5%
Other values (17) 15480
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45373
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 4807
 
10.6%
O 3608
 
8.0%
A 3589
 
7.9%
R 3114
 
6.9%
I 2781
 
6.1%
D 2672
 
5.9%
N 2483
 
5.5%
C 2458
 
5.4%
S 2345
 
5.2%
M 2036
 
4.5%
Other values (17) 15480
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45373
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 4807
 
10.6%
O 3608
 
8.0%
A 3589
 
7.9%
R 3114
 
6.9%
I 2781
 
6.1%
D 2672
 
5.9%
N 2483
 
5.5%
C 2458
 
5.4%
S 2345
 
5.2%
M 2036
 
4.5%
Other values (17) 15480
34.1%

model
Text

Distinct2053
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size496.5 KiB
2025-08-18T14:47:37.512423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length41
Median length32
Mean length11.831957
Min length2

Characters and Unicode

Total characters87379
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique502 ?
Unique (%)6.8%

Sample

1st rowILX
2nd rowILX
3rd rowILX HYBRID
4th rowMDX 4WD
5th rowRDX AWD
ValueCountFrequency (%)
awd 1128
 
6.8%
ffv 592
 
3.6%
4wd 477
 
2.9%
coupe 375
 
2.3%
4x4 333
 
2.0%
s 326
 
2.0%
4matic 239
 
1.4%
cabriolet 221
 
1.3%
xdrive 215
 
1.3%
cooper 204
 
1.2%
Other values (709) 12464
75.2%
2025-08-18T14:47:37.758235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9200
 
10.5%
A 5815
 
6.7%
R 4636
 
5.3%
E 4496
 
5.1%
C 3621
 
4.1%
T 3510
 
4.0%
O 3450
 
3.9%
D 3182
 
3.6%
S 3165
 
3.6%
I 2352
 
2.7%
Other values (59) 43952
50.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9200
 
10.5%
A 5815
 
6.7%
R 4636
 
5.3%
E 4496
 
5.1%
C 3621
 
4.1%
T 3510
 
4.0%
O 3450
 
3.9%
D 3182
 
3.6%
S 3165
 
3.6%
I 2352
 
2.7%
Other values (59) 43952
50.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9200
 
10.5%
A 5815
 
6.7%
R 4636
 
5.3%
E 4496
 
5.1%
C 3621
 
4.1%
T 3510
 
4.0%
O 3450
 
3.9%
D 3182
 
3.6%
S 3165
 
3.6%
I 2352
 
2.7%
Other values (59) 43952
50.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9200
 
10.5%
A 5815
 
6.7%
R 4636
 
5.3%
E 4496
 
5.1%
C 3621
 
4.1%
T 3510
 
4.0%
O 3450
 
3.9%
D 3182
 
3.6%
S 3165
 
3.6%
I 2352
 
2.7%
Other values (59) 43952
50.3%

vehicle_class
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size494.8 KiB
SUV - SMALL
1217 
MID-SIZE
1133 
COMPACT
1022 
SUV - STANDARD
735 
FULL-SIZE
639 
Other values (11)
2639 

Length

Max length24
Median length21
Mean length11.587407
Min length7

Characters and Unicode

Total characters85573
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOMPACT
2nd rowCOMPACT
3rd rowCOMPACT
4th rowSUV - SMALL
5th rowSUV - SMALL

Common Values

ValueCountFrequency (%)
SUV - SMALL 1217
16.5%
MID-SIZE 1133
15.3%
COMPACT 1022
13.8%
SUV - STANDARD 735
10.0%
FULL-SIZE 639
8.7%
SUBCOMPACT 606
8.2%
PICKUP TRUCK - STANDARD 538
7.3%
TWO-SEATER 460
 
6.2%
MINICOMPACT 326
 
4.4%
STATION WAGON - SMALL 252
 
3.4%
Other values (6) 457
 
6.2%

Length

2025-08-18T14:47:37.838645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3042
20.8%
suv 1952
13.3%
small 1628
11.1%
standard 1273
8.7%
mid-size 1186
 
8.1%
compact 1022
 
7.0%
pickup 697
 
4.8%
truck 697
 
4.8%
full-size 639
 
4.4%
subcompact 606
 
4.1%
Other values (11) 1883
12.9%

Most occurring characters

ValueCountFrequency (%)
S 8335
 
9.7%
A 7531
 
8.8%
7240
 
8.5%
C 5478
 
6.4%
T 5454
 
6.4%
- 5327
 
6.2%
M 5174
 
6.0%
I 4979
 
5.8%
L 4688
 
5.5%
U 4668
 
5.5%
Other values (14) 26699
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85573
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 8335
 
9.7%
A 7531
 
8.8%
7240
 
8.5%
C 5478
 
6.4%
T 5454
 
6.4%
- 5327
 
6.2%
M 5174
 
6.0%
I 4979
 
5.8%
L 4688
 
5.5%
U 4668
 
5.5%
Other values (14) 26699
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85573
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 8335
 
9.7%
A 7531
 
8.8%
7240
 
8.5%
C 5478
 
6.4%
T 5454
 
6.4%
- 5327
 
6.2%
M 5174
 
6.0%
I 4979
 
5.8%
L 4688
 
5.5%
U 4668
 
5.5%
Other values (14) 26699
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85573
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 8335
 
9.7%
A 7531
 
8.8%
7240
 
8.5%
C 5478
 
6.4%
T 5454
 
6.4%
- 5327
 
6.2%
M 5174
 
6.0%
I 4979
 
5.8%
L 4688
 
5.5%
U 4668
 
5.5%
Other values (14) 26699
31.2%

engine_size_l_
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1600677
Minimum0.9
Maximum8.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-08-18T14:47:37.946598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.5
Q12
median3
Q33.7
95-th percentile6
Maximum8.4
Range7.5
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.3541705
Coefficient of variation (CV)0.42852577
Kurtosis-0.13196328
Mean3.1600677
Median Absolute Deviation (MAD)1
Skewness0.80918099
Sum23337.1
Variance1.8337776
MonotonicityNot monotonic
2025-08-18T14:47:38.050677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1460
19.8%
3 804
 
10.9%
3.6 536
 
7.3%
3.5 529
 
7.2%
2.5 423
 
5.7%
2.4 346
 
4.7%
1.6 302
 
4.1%
5.3 290
 
3.9%
1.8 216
 
2.9%
1.4 211
 
2.9%
Other values (41) 2268
30.7%
ValueCountFrequency (%)
0.9 3
 
< 0.1%
1 18
 
0.2%
1.2 25
 
0.3%
1.3 11
 
0.1%
1.4 211
 
2.9%
1.5 207
 
2.8%
1.6 302
 
4.1%
1.8 216
 
2.9%
2 1460
19.8%
2.1 5
 
0.1%
ValueCountFrequency (%)
8.4 5
 
0.1%
8 3
 
< 0.1%
6.8 8
 
0.1%
6.7 25
 
0.3%
6.6 29
 
0.4%
6.5 18
 
0.2%
6.4 46
 
0.6%
6.3 3
 
< 0.1%
6.2 162
2.2%
6 94
1.3%

cylinders
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6150305
Minimum3
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-08-18T14:47:38.111730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q14
median6
Q36
95-th percentile8
Maximum16
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8283065
Coefficient of variation (CV)0.32560937
Kurtosis1.525175
Mean5.6150305
Median Absolute Deviation (MAD)2
Skewness1.1104154
Sum41467
Variance3.3427047
MonotonicityNot monotonic
2025-08-18T14:47:38.178547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 3220
43.6%
6 2446
33.1%
8 1402
19.0%
12 151
 
2.0%
3 95
 
1.3%
10 42
 
0.6%
5 26
 
0.4%
16 3
 
< 0.1%
ValueCountFrequency (%)
3 95
 
1.3%
4 3220
43.6%
5 26
 
0.4%
6 2446
33.1%
8 1402
19.0%
10 42
 
0.6%
12 151
 
2.0%
16 3
 
< 0.1%
ValueCountFrequency (%)
16 3
 
< 0.1%
12 151
 
2.0%
10 42
 
0.6%
8 1402
19.0%
6 2446
33.1%
5 26
 
0.4%
4 3220
43.6%
3 95
 
1.3%

transmission
Real number (ℝ)

Missing 

Distinct7
Distinct (%)0.1%
Missing295
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean6.8866008
Minimum4
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-08-18T14:47:38.237044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q16
median6
Q38
95-th percentile9
Maximum10
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2092899
Coefficient of variation (CV)0.17560041
Kurtosis-0.26719351
Mean6.8866008
Median Absolute Deviation (MAD)1
Skewness0.54552998
Sum48826
Variance1.4623822
MonotonicityNot monotonic
2025-08-18T14:47:38.290030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 3259
44.1%
8 1802
24.4%
7 1026
 
13.9%
9 419
 
5.7%
5 307
 
4.2%
10 210
 
2.8%
4 67
 
0.9%
(Missing) 295
 
4.0%
ValueCountFrequency (%)
4 67
 
0.9%
5 307
 
4.2%
6 3259
44.1%
7 1026
 
13.9%
8 1802
24.4%
9 419
 
5.7%
10 210
 
2.8%
ValueCountFrequency (%)
10 210
 
2.8%
9 419
 
5.7%
8 1802
24.4%
7 1026
 
13.9%
6 3259
44.1%
5 307
 
4.2%
4 67
 
0.9%

fuel_type
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size418.4 KiB
X
3637 
Z
3202 
E
370 
D
 
175
N
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7385
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowZ
2nd rowZ
3rd rowZ
4th rowZ
5th rowZ

Common Values

ValueCountFrequency (%)
X 3637
49.2%
Z 3202
43.4%
E 370
 
5.0%
D 175
 
2.4%
N 1
 
< 0.1%

Length

2025-08-18T14:47:38.365818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-18T14:47:38.411642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
x 3637
49.2%
z 3202
43.4%
e 370
 
5.0%
d 175
 
2.4%
n 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
X 3637
49.2%
Z 3202
43.4%
E 370
 
5.0%
D 175
 
2.4%
N 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7385
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
X 3637
49.2%
Z 3202
43.4%
E 370
 
5.0%
D 175
 
2.4%
N 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7385
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
X 3637
49.2%
Z 3202
43.4%
E 370
 
5.0%
D 175
 
2.4%
N 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7385
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
X 3637
49.2%
Z 3202
43.4%
E 370
 
5.0%
D 175
 
2.4%
N 1
 
< 0.1%

fuel_consumption_city_l_100_km_
Real number (ℝ)

High correlation 

Distinct211
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.556534
Minimum4.2
Maximum30.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-08-18T14:47:38.464676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.2
5-th percentile8
Q110.1
median12.1
Q314.6
95-th percentile19.2
Maximum30.6
Range26.4
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.5002741
Coefficient of variation (CV)0.27876118
Kurtosis1.196145
Mean12.556534
Median Absolute Deviation (MAD)2.2
Skewness0.80900471
Sum92730
Variance12.251919
MonotonicityNot monotonic
2025-08-18T14:47:38.549893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.8 125
 
1.7%
12.4 123
 
1.7%
11.8 120
 
1.6%
11.9 119
 
1.6%
10.6 109
 
1.5%
10.2 108
 
1.5%
11.3 107
 
1.4%
10.5 104
 
1.4%
12.1 104
 
1.4%
11.2 102
 
1.4%
Other values (201) 6264
84.8%
ValueCountFrequency (%)
4.2 5
0.1%
4.3 4
0.1%
4.4 5
0.1%
4.5 8
0.1%
4.6 9
0.1%
4.7 3
 
< 0.1%
4.8 3
 
< 0.1%
4.9 8
0.1%
5 4
0.1%
5.1 4
0.1%
ValueCountFrequency (%)
30.6 2
< 0.1%
30.3 2
< 0.1%
30.2 2
< 0.1%
30 2
< 0.1%
26.8 3
< 0.1%
26.7 1
 
< 0.1%
26.6 2
< 0.1%
26.3 1
 
< 0.1%
26.2 1
 
< 0.1%
25.7 2
< 0.1%

fuel_consumption_hwy_l_100_km_
Real number (ℝ)

High correlation 

Distinct143
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0417062
Minimum4
Maximum20.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-08-18T14:47:38.672073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6.1
Q17.5
median8.7
Q310.2
95-th percentile13.2
Maximum20.6
Range16.6
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation2.2244564
Coefficient of variation (CV)0.24602175
Kurtosis2.0089689
Mean9.0417062
Median Absolute Deviation (MAD)1.3
Skewness1.0792167
Sum66773
Variance4.9482062
MonotonicityNot monotonic
2025-08-18T14:47:38.761231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.8 209
 
2.8%
8.5 184
 
2.5%
8.7 169
 
2.3%
8.3 168
 
2.3%
8.4 162
 
2.2%
9.6 154
 
2.1%
7.7 152
 
2.1%
9.2 151
 
2.0%
7 150
 
2.0%
9 146
 
2.0%
Other values (133) 5740
77.7%
ValueCountFrequency (%)
4 4
 
0.1%
4.2 1
 
< 0.1%
4.4 3
 
< 0.1%
4.5 2
 
< 0.1%
4.6 6
 
0.1%
4.7 1
 
< 0.1%
4.8 7
0.1%
4.9 10
0.1%
5 8
0.1%
5.1 16
0.2%
ValueCountFrequency (%)
20.6 2
 
< 0.1%
20.5 5
0.1%
20.4 2
 
< 0.1%
20 1
 
< 0.1%
19.6 1
 
< 0.1%
19.3 4
0.1%
19.2 1
 
< 0.1%
18.8 2
 
< 0.1%
18.6 1
 
< 0.1%
18.5 5
0.1%

fuel_consumption_comb_l_100_km_
Real number (ℝ)

High correlation 

Distinct181
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.975071
Minimum4.1
Maximum26.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-08-18T14:47:38.854308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.1
5-th percentile7.2
Q18.9
median10.6
Q312.6
95-th percentile16.5
Maximum26.1
Range22
Interquartile range (IQR)3.7

Descriptive statistics

Standard deviation2.8925063
Coefficient of variation (CV)0.2635524
Kurtosis1.3935754
Mean10.975071
Median Absolute Deviation (MAD)1.8
Skewness0.89331572
Sum81050.9
Variance8.3665927
MonotonicityNot monotonic
2025-08-18T14:47:38.943812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.4 145
 
2.0%
8.4 136
 
1.8%
9.8 135
 
1.8%
9.1 132
 
1.8%
10.3 130
 
1.8%
8.7 128
 
1.7%
11 127
 
1.7%
9.9 125
 
1.7%
10.7 124
 
1.7%
9 121
 
1.6%
Other values (171) 6082
82.4%
ValueCountFrequency (%)
4.1 4
 
0.1%
4.2 1
 
< 0.1%
4.3 2
 
< 0.1%
4.4 2
 
< 0.1%
4.5 5
0.1%
4.7 9
0.1%
4.8 7
0.1%
4.9 6
0.1%
5 5
0.1%
5.1 12
0.2%
ValueCountFrequency (%)
26.1 2
< 0.1%
25.9 2
< 0.1%
25.8 2
< 0.1%
25.7 2
< 0.1%
23.9 1
 
< 0.1%
23 1
 
< 0.1%
22.6 4
0.1%
22.5 1
 
< 0.1%
22.2 3
< 0.1%
22.1 2
< 0.1%

fuel_consumption_comb_mpg_
Real number (ℝ)

High correlation 

Distinct54
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.481652
Minimum11
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-08-18T14:47:39.045600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile17
Q122
median27
Q332
95-th percentile39
Maximum69
Range58
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.2318792
Coefficient of variation (CV)0.263153
Kurtosis2.499369
Mean27.481652
Median Absolute Deviation (MAD)5
Skewness0.97703406
Sum202952
Variance52.300076
MonotonicityNot monotonic
2025-08-18T14:47:39.127585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 482
 
6.5%
29 470
 
6.4%
27 461
 
6.2%
22 453
 
6.1%
26 445
 
6.0%
24 399
 
5.4%
23 359
 
4.9%
31 358
 
4.8%
30 333
 
4.5%
34 327
 
4.4%
Other values (44) 3298
44.7%
ValueCountFrequency (%)
11 8
 
0.1%
12 6
 
0.1%
13 26
 
0.4%
14 30
 
0.4%
15 67
 
0.9%
16 134
1.8%
17 161
2.2%
18 159
2.2%
19 265
3.6%
20 311
4.2%
ValueCountFrequency (%)
69 4
 
0.1%
67 1
 
< 0.1%
66 2
 
< 0.1%
64 2
 
< 0.1%
63 5
0.1%
60 9
0.1%
59 7
0.1%
58 6
0.1%
56 5
0.1%
55 12
0.2%

co2_emissions_g_km_
Real number (ℝ)

High correlation 

Distinct331
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.5847
Minimum96
Maximum522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.8 KiB
2025-08-18T14:47:39.242028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum96
5-th percentile169
Q1208
median246
Q3288
95-th percentile354
Maximum522
Range426
Interquartile range (IQR)80

Descriptive statistics

Standard deviation58.512679
Coefficient of variation (CV)0.2335046
Kurtosis0.47880085
Mean250.5847
Median Absolute Deviation (MAD)40
Skewness0.52609381
Sum1850568
Variance3423.7336
MonotonicityNot monotonic
2025-08-18T14:47:39.346671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242 85
 
1.2%
221 82
 
1.1%
230 77
 
1.0%
214 77
 
1.0%
294 76
 
1.0%
232 76
 
1.0%
258 75
 
1.0%
253 75
 
1.0%
246 75
 
1.0%
209 74
 
1.0%
Other values (321) 6613
89.5%
ValueCountFrequency (%)
96 4
0.1%
99 1
 
< 0.1%
102 1
 
< 0.1%
103 1
 
< 0.1%
104 2
 
< 0.1%
105 3
< 0.1%
106 2
 
< 0.1%
108 2
 
< 0.1%
109 2
 
< 0.1%
110 7
0.1%
ValueCountFrequency (%)
522 3
< 0.1%
493 2
< 0.1%
488 1
 
< 0.1%
487 1
 
< 0.1%
485 1
 
< 0.1%
476 1
 
< 0.1%
473 1
 
< 0.1%
467 1
 
< 0.1%
465 3
< 0.1%
464 2
< 0.1%

Interactions

2025-08-18T14:47:36.494885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:32.450412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:33.194188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:33.751465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:34.298632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:34.836796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:35.423772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:35.944053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:36.565984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:32.516845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:33.270635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:33.816959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:34.348518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:34.914206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:35.488870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:36.011331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:36.621791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:32.595583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:33.343313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:33.882430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:34.440882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:34.989676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:35.555648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:36.080334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:36.696388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:32.678712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:33.406549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:33.951592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:34.498453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:35.066803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:35.613789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:36.148848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:36.758630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:32.750449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:33.479589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:34.028087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:34.565062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:35.138530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:35.680939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:36.210436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:36.819260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:32.840801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:33.551527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:34.091867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:34.631563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:35.205398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:35.750242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:36.284180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:36.879551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:32.911392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:33.616026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:34.164119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:34.698196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:35.280601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:35.814096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:36.346651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:37.062570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:32.976929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:33.682054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:34.232116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:34.774358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:35.352874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:35.886036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-18T14:47:36.429568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-18T14:47:39.416830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
co2_emissions_g_km_cylindersengine_size_l_fuel_consumption_city_l_100_km_fuel_consumption_comb_l_100_km_fuel_consumption_comb_mpg_fuel_consumption_hwy_l_100_km_fuel_typemaketransmissionvehicle_class
co2_emissions_g_km_1.0000.8520.8690.9560.963-0.9620.9410.1640.3810.1980.285
cylinders0.8521.0000.9360.8470.834-0.8330.7820.1810.6250.2650.286
engine_size_l_0.8690.9361.0000.8720.862-0.8610.8160.2420.4990.2180.262
fuel_consumption_city_l_100_km_0.9560.8470.8721.0000.994-0.9930.9490.3250.3550.1720.291
fuel_consumption_comb_l_100_km_0.9630.8340.8620.9941.000-0.9990.9770.3350.3370.1650.297
fuel_consumption_comb_mpg_-0.962-0.833-0.861-0.993-0.9991.000-0.9760.3300.296-0.1640.262
fuel_consumption_hwy_l_100_km_0.9410.7820.8160.9490.977-0.9761.0000.3320.2770.1400.311
fuel_type0.1640.1810.2420.3250.3350.3300.3321.0000.4470.2700.296
make0.3810.6250.4990.3550.3370.2960.2770.4471.0000.4740.359
transmission0.1980.2650.2180.1720.165-0.1640.1400.2700.4741.0000.345
vehicle_class0.2850.2860.2620.2910.2970.2620.3110.2960.3590.3451.000

Missing values

2025-08-18T14:47:37.164889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-18T14:47:37.255747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

makemodelvehicle_classengine_size_l_cylinderstransmissionfuel_typefuel_consumption_city_l_100_km_fuel_consumption_hwy_l_100_km_fuel_consumption_comb_l_100_km_fuel_consumption_comb_mpg_co2_emissions_g_km_
0ACURAILXCOMPACT2.045.0Z9.96.78.533196
1ACURAILXCOMPACT2.446.0Z11.27.79.629221
2ACURAILX HYBRIDCOMPACT1.547.0Z6.05.85.948136
3ACURAMDX 4WDSUV - SMALL3.566.0Z12.79.111.125255
4ACURARDX AWDSUV - SMALL3.566.0Z12.18.710.627244
5ACURARLXMID-SIZE3.566.0Z11.97.710.028230
6ACURATLMID-SIZE3.566.0Z11.88.110.128232
7ACURATL AWDMID-SIZE3.766.0Z12.89.011.125255
8ACURATL AWDMID-SIZE3.766.0Z13.49.511.624267
9ACURATSXCOMPACT2.445.0Z10.67.59.231212
makemodelvehicle_classengine_size_l_cylinderstransmissionfuel_typefuel_consumption_city_l_100_km_fuel_consumption_hwy_l_100_km_fuel_consumption_comb_l_100_km_fuel_consumption_comb_mpg_co2_emissions_g_km_
7375VOLVOS90 T6 AWDMID-SIZE2.048.0Z11.37.59.629223
7376VOLVOV60 T5STATION WAGON - SMALL2.048.0Z10.57.18.932208
7377VOLVOV60 T6 AWDSTATION WAGON - SMALL2.048.0Z11.07.49.430219
7378VOLVOV60 CC T5 AWDSTATION WAGON - SMALL2.048.0Z10.87.79.430220
7379VOLVOXC40 T4 AWDSUV - SMALL2.048.0X10.27.59.031210
7380VOLVOXC40 T5 AWDSUV - SMALL2.048.0Z10.77.79.430219
7381VOLVOXC60 T5 AWDSUV - SMALL2.048.0Z11.28.39.929232
7382VOLVOXC60 T6 AWDSUV - SMALL2.048.0Z11.78.610.327240
7383VOLVOXC90 T5 AWDSUV - STANDARD2.048.0Z11.28.39.929232
7384VOLVOXC90 T6 AWDSUV - STANDARD2.048.0Z12.28.710.726248

Duplicate rows

Most frequently occurring

makemodelvehicle_classengine_size_l_cylinderstransmissionfuel_typefuel_consumption_city_l_100_km_fuel_consumption_hwy_l_100_km_fuel_consumption_comb_l_100_km_fuel_consumption_comb_mpg_co2_emissions_g_km_# duplicates
623LEXUSGS FCOMPACT5.088.0Z14.99.712.5232935
197CHRYSLER300FULL-SIZE3.668.0X12.47.810.3272424
200CHRYSLER300 AWDFULL-SIZE3.668.0X12.88.711.0262584
286FIAT500LSTATION WAGON - SMALL1.446.0X10.77.99.4302214
635LEXUSNX 300h AWDSUV - SMALL2.546.0X7.27.97.5381764
638LEXUSRC FSUBCOMPACT5.088.0Z15.29.512.6222894
640LEXUSRX 350 AWDSUV - SMALL3.568.0X12.29.010.8262524
643LEXUSRX 450h AWDSUV - STANDARD3.566.0Z7.58.47.9361854
794MITSUBISHIRVR 4WDSUV - SMALL2.046.0X10.18.29.2312134
798NISSAN370ZTWO-SEATER3.767.0Z12.69.311.1252614